Negative Examples Research Articles

Graph Community Infomax

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.

Анализ распространенности курения среди лиц молодого возраста с учетом социально-экономических и психоэмоциональных факторов

The article is devoted to the study of factors contributing to the formation of adherence to smoking in young people. The study involved 300 young people, of which 154 people are students of the Saratov College of Water Transport, Construction and Service, 146 people are students of a medical university. The study of smoking prevalence was carried out using adapted questionnaires that include questions about adherence to tobacco smoking, as well as modern alternative means of smoking (electronic cigarettes), awareness of the dangers of smoking tobacco products and electronic cigarettes. Were studied the socio-economic and psychological factors that contribute to the formation of addiction. The results of the study indicate a high prevalence of smoking among young people surveyed, while more than half of all respondents who smoke prefer alternative means of smoking. It should be noted that there is a higher prevalence of alternative means of smoking among girls compared to boys. It was found that smoking electronic cigarettes prevails among people with a higher level of material well-being. The results of studying the awareness of respondents about the dangers of tobacco smoking showed a high level of awareness of the respondents. At the same time, awareness of the dangers of alternative means of smoking is at a lower level. An analysis of the study of the reasons for starting smoking among study participants showed that persons living separately from their parents are more susceptible to smoking. According to the respondents themselves, an important role in the formation of adherence to an addiction is played by a negative example of the social environment (smoking of parents, friends, acquaintances). The correlation analysis revealed the relationship between smoking and the level of psychoemotional stress. Thus, the result of this study indicates that the high prevalence of smoking among young people is due to the impact of socioeconomic and psychological factors that must be taken into account in the fight against the formation of addiction among the younger generation. English version of the article is available at URL: https://panor.ru/articles/analysis-of-the-prevalence-of-smoking-among-young-people-taking-into-account-socio-economic-and-psycho-emotional-factors/75548.html

Early-fusion based pulsar identification with smart under-sampling

The discovery of pulsars is of great significance in the field of physics and astronomy. As the astronomical equipment produces a large number of pulsar data, an algorithm for automatically identifying pulsars becomes urgent. We propose a deep learning framework for pulsar recognition. In response to the extreme imbalance between positive and negative examples and the hard negative sample issue presented in the High Time Resolution Universe Medlat Training Data, there are two coping strategies in our framework: the smart under-sampling and the improved loss function. We also apply the early-fusion strategy to integrate features obtained from different attributes before classification to improve the performance. To our best knowledge, this is the first study that integrates these strategies and techniques in pulsar recognition. The experiment results show that our framework outperforms previous works with respect to either the training time or F1 score. We can not only speed up the training time by 10 × compared with the state-of-the-art work, but also get a competitive result in terms of F1 score.

SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions.

Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/.

Siblings' similarities and dissimilarities: A review of theoretical perspectives and empirical insights

AbstractAlthough siblings may differ considerably, the similarities between them are often an important source of emotional support in one's life and influence one's life course trajectories. In this review on the topic of sibling relationship and cross‐sibling effect interactions, we aim to encourage research interest and facilitate knowledge building. We begin our review by highlighting how the parental home may induce differentiation between siblings. Next, we illustrate the theories explaining sibling similarities and differences and discuss the factors that stimulate these. Throughout the review, we do not only highlight the complex mechanisms by which siblings imitate yet differentiate themselves, but also mutually relate to their life courses and education. New understandings of how similarities between siblings can simultaneously act as powerful influences and negative examples are provided.

A Semi-Supervised Approach to GRN Inference Using Learning and Optimization

Gene regulatory network (GRN) inference is a challenging problem that lends itself to a learning task. Both positive and negative examples are needed to perform supervised and semi-supervised learning. However, GRN datasets include only positive examples and/or unlabeled ones. Recently a growing interest is being devoted to the generation of negative examples from unlabeled data. Within this context, the authors propose to generate potential negative examples from the set of unlabeled ones and keep those that lead to the best classification accuracy when used with positive examples. A new proposed genetic algorithm for fixed-size subset selection has been combined with a support vector machine model for this purpose. The authors assessed the performance of the proposed approach using simulated and experimental datasets. Using simulated datasets, the proposed approach outperforms the other methods in most cases and improves the performance metrics when using balanced data. Experimental datasets show that the proposed approach allows finding the optimal solution for each transcription factor in this study.

Polynomially Decomposable Global Cost Functions in Weighted Constraint Satisfaction

In maintaining consistencies, such as GAC*, FDGAC* and weak EDGAC*, for global cost functions, Weighted CSP (WCSP) solvers rely on the projection and extension operations, which entail the computation of the cost functions' minima. Tractability of this minimum computation is essential for efficient execution. Since projections/extensions modify the cost functions, an important issue is tractable projection-safety, concerning whether minimum cost computation remains tractable after projections/extensions. In this paper, we prove that tractable projection-safety is always possible for projections/extensions to/from the nullary cost function (W0), and always impossible for projections/extensions to/from n-ary cost functions for n > = 2. When n = 1, the answer is indefinite. We give a simple negative example, while Lee and Leung's flow-based projection-safe cost functions are also tractable projection-safe. We propose polynomially decomposable cost functions, which are amenable to tractable minimum computation. We further prove that the polynomial decomposability property is unaffected by projections/extensionsto/from unary cost functions. Thus, polynomially decomposable cost functions are tractable projection-safe. We show that the SOFT_AMONG, SOFT_REGULAR, SOFT_GRAMMAR and MAX_WEIGHT/MIN_WEIGHT are polynomially decomposable. They are embedded in a WCSP solver for extensive experiments to confirm the feasibility and efficiency of our proposal.

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.

You Have the Big Idea, Concept, and Some Examples ... Now What?

Conducting a concept or content analysis is essential in planning efficient effective instruction. Sequencing instruction when teaching a concept is equally essential. Through instructional sequencing, the upfront analysis of identifying critical and variable attributes comes to life and makes for a successful teaching arrangement. Learning concepts can be made easier or more difficult based on the order in which instructional stimuli are introduced and described. In this article we will describe Direct Instruction's emphasis on clear instruction (faultless communication) and its method for sequencing and arranging positive and negative examples (juxtaposition). We will demonstrate how Direct Instruction's five principles of juxtaposition inform examples should be presented to maximize student learning.

Negative sampling strategies for contrastive self-supervised learning of graph representations

Contrastive learning has become a successful approach for learning powerful text and image representations in a self-supervised manner. Contrastive frameworks learn to distinguish between representations coming from augmentations of the same data point (positive pairs) and those of other (negative) examples. Recent studies aim at extending methods from contrastive learning to graph data. In this work, we propose a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL). It learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. We investigate different standard and new negative sampling strategies as well as a comparison without negative sampling approach. We demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks in both transductive and inductive learning setups.

Some Examples of the Phenomenon of Metadidactic Slippage in School Practice

Background: In didactics of mathematics, the problem of metadidactic slippage ( glissement metadidactique ) evidenced by Guy Brousseau has been shown for decades. But the school didactic practice proposes behavioural models (mathematics teaching-learning) from which it is manifest that the subject is completely unknown. Objectives: This article intends to present and discuss the metadidactic slippage problem and give some negative examples of its influence, in particular, about the naive interpretation of the so-called Pólya heuristic regarding problem solving in mathematics. Design: Theoretical research in didactics of mathematics. Setting and participants: focuses on the school didactic practice of problem solving in mathematics. Data collection and analysis: Negative examples chosen from among those most diffused in the school world are analysed in the light of modern didactics of mathematics to identify metadidactic slippage in them. Results: Thanks to the slippage, the student learns a scheme, or an algorithm, not the desired mathematical topic T, which remains a mystery to the student (and sometimes also to the teacher). Conclusions: Before trying to “improve” the teaching-learning of mathematics with temporary and drastic measures, it is better, at least, to study it modestly.

Ensemble learning with siamese networks for visual tracking

Ensemble learning (EL) is an effective and commonly used technique to improve visual tasks’ accuracy, such as classification and detection. However, EL is rarely used in visual tracking. To fill this knowledge gap, we first have completed some research to investigate why knowledge distillation was ineffective in visual tracking tasks. Comparing the difference between the classification and visual tracking, conclusions are given: (i) Numerous simple negative examples are redundant, while only a few hard negative samples are valid for visual tracking knowledge distillation. (ii) The hint knowledge flows differently between classification and visual tracking. To solve the above problems, we design two new loss functions and integrate them into the proposed Ensemble Learning (EL) framework that can be employed in Siamese architectures such as SiamFC, SiamRPN, SiamFC+, and SiamRPN+. The EL treats two Siamese networks as students and enables them to learn collaboratively. A better solution is yielded by the EL framework than training students individually. Experiments on OTB-2013, OTB-2015, VOT2015, VOT2016, VOT2017, VOT2018, LaSOT and TrackingNet have verified the effectiveness of our proposed technique on boosting the performance for the four Siamese algorithms. The EL-SiamRPN+ achieves leading performance in the challenges.

Artificial Illumination of Memorial Complexes Reconstruction and Creation of a New One

Memorial architecture is one of the most important resources for self-identification of society and the state. Recently, there has been a renewed interest of the population and tourists in memorial and museum complexes, and their attendance has increased. Old objects are being actively reconstructed, new ones are being built. The paper provides positive and negative examples of architectural illumination of memorial complexes. The techniques and means of lighting design are considered by the example of a number of reconstructed and newly created lighting installations.

GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules

ABSTRACT Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84.

Domestic and International Experience in Researching Police Officers’ Motivation

The article is focused on the consideration of the motivation of police officers of the National Police of Ukraine as determinants of professional efficiency. The authors have analyzed the domestic and international experience of studying the professional motivation of police officers and the possibilities of its practical use in the system of psychological provision of the employees of the National Police of Ukraine. The levels and components of motivation, directions of its improvement have been studied. The authors have accomplished review of research on the motives for choosing a service in the police as a profession, as well as the factors of further motivation of the employees. The relationship between individual motivation, learning productivity, work and police officers’ satisfaction with the work has been traced. The role of self-motivation and self-organization in increasing the efficiency of professional activity has been determined.
 Motivating factors have been provided – achievement, recognition, responsibility, promotion and growth, etc., means to support motivation, as well as demotivators – stress due to internal factors, poor health and physical well-being, a negative example of employees who lost interest in the service, etc. The importance of organizational justice and personal example of the head, his awareness and attention to the factors of individual motivation and productivity of the employee for the optimization of the police activity has been substantiated.
 The perspective of developing diagnostic tools and development of motivation of police personnel in the field of work with psycho-technologies of diagnostic, therapeutic, normative and influential orientation has been outlined.

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

BackgroundA growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research.ResultsInspired by the work of predecessors, we discover that the noise hiding in the data can affect the prediction performance and then propose an anti-noise algorithm (ANMDA) to predict potential miRNA-disease associations. Firstly, we calculate the similarity in miRNAs and diseases to construct features and obtain positive samples according to the Human MicroRNA Disease Database version 2.0 (HMDD v2.0). Then, we apply k-means on the undetected miRNA-disease associations and sample the negative examples equally from the k-cluster. Further, we construct several data subsets through sampling with replacement to feed on the light gradient boosting machine (LightGBM) method. Finally, the voting method is applied to predict potential miRNA-disease relationships. As a result, ANMDA can achieve an area under the receiver operating characteristic curve (AUROC) of 0.9373 ± 0.0005 in five-fold cross-validation, which is superior to several published methods. In addition, we analyze the predicted miRNA-disease associations with high probability and compare them with the data in HMDD v3.0 in the case study. The results show ANMDA is a novel and practical algorithm that can be used to infer potential miRNA-disease associations.ConclusionThe results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise.

Improved Face Recognition With Fractal-Based Texture Analysis

Fractals are useful to uniquely represent texture in the human face, which serves as an equivalent of human vision. FaceNet, calculating face descriptors of a person, has been observed to perform with setbacks when several factors of occlusion are present. This paper proposes a new methodology that exploits the self-similar patterns in a person's face to highlight and enhance regions of high texture in a facial image. The system maps the original image into a representation in the pre-processing stage of computer vision. This representation when fed as an input to the FaceNet CNN optimizes the face embedding generated. An SVM classifier separates the hard positive examples from the hard negative examples during classification. The model is trained using YouTube Faces DB as primary dataset and for validation; a custom dataset is designed to verify a person's identity despite the presence of secondary factors such as expressions and forgery. The proposed model attained an overall accuracy of 96.73% with the YouTube Faces DB, and a notable reduction in the false positive rates is observed.

국경을 넘나들던 친일파 - 이완용과 근대 중국의 매국노 담론(1905~1945) -

Lee Wanyong is not only the well-known quisling in Korea, but also the byword of “quisling” in modern China, and the name is used as a negative example to blame the traitors and to caution the masses. From the end of Qing Dynasty to the early days of the Republic of China, when external crises of China continued and even got worse, the production of knowledge and discourses about Lee Wanyong continued as well. Especially after 1915, as the confliction with Japan gradually rose to the principal contradiction of Chinese external relations, Lee Wonyong as a label finally changed from general reference of powers that fawning to foreign countries to a byword that specifically means pro-Japanese faction.BR On the background of external crisis interwoven with internal faction, various public opinions and factional powers used Lee Wonyong to refer to the objects that they tried to attack, so that a large number of Lee Wanyong of Chinese version was produced. In other words, Lee Wanyong largely became a symbol of political rhetoric and stigmatization under the factional background in China. Therefore, the process of the production of quislings who are like Lee Wanyong is a part of how anti-Japanese nationalism built the history in modern China.BR Nonetheless, it is improper and not in line with historical facts to put the blame for the falling of Choson on individual “traitors” such as Lee Wonyong. Quite different from the rough argument that the public opinions often refer Lee Wonyong as quisling and put the blame on specific person for the falling crisis of a nation, a few intellectuals analyzed the ending of Choson at the internal level, such as Korean social structure, national character, etc. The analyzation made at that time reflected the anxiety caused by the internal and external crises in China of the days and “Sinocentrism” complex left by the traditional suzerain-vassal order, but compared with outpouring the feelings by holding specific quislings accountable and the roaring public voice from the flaring anger among people, this kind of discourse actually appears more sober and objective to reflect on the colonial history of Choson and the social crises of modern China.

Comprehensive Assessment of Flow and Other Analytical Methods Dedicated to the Determination of Zinc in Water.

An original strategy to evaluate analytical procedures is proposed and applied to verify if the flow-based methods, generally favorable in terms of green chemistry, are competitive when their evaluation also relies on other criteria. To this end, eight methods for the determination of zinc in waters, including four flow-based ones, were compared and the Red–Green–Blue (RGB) model was exploited. This model takes into account several features related to the general quality of an analytical method, namely, its analytical efficiency, compliance with the green analytical chemistry, as well as practical and economic usefulness. Amongst the investigated methods, the best was the flow-based spectrofluorimetric one, and a negative example was that one involving a flow module, ICP ionization and MS detection, which was very good in analytical terms, but worse in relation to other aspects, which significantly limits its overall potential. Good assessments were also noted for non-flow electrochemical methods, which attract attention with a high degree of balance of features and, therefore, high versatility. The original attempt to confront several worldwide accepted analytical strategies, although to some extent subjective and with limitations, provides interesting information and indications, establishing a novel direction towards the development and evaluation of analytical methods.

TransExplain: Using neural networks to find suitable explanations for Chinese phrases

When reading an article, especially a professional article, we often encounter words or phrases that we don't recognize. They may be specific domain terms or emerging entities. When we can't guess their meaning from the article, we generally refer to the terminology dictionary or search for related content on the Internet to understand them. Some researchers have used natural language generation (NLG) models to explain these unknown phrases in recent years automatically. Still, current NLG models have difficulties generating long sentences with good coherence, and they are difficult to generate multiple sentences that describe unknown phrases from different angles. Therefore, this paper proposes a model that can judge whether an existing sentence can explain a certain phrase, called TransExplain. TransExplain can use LSTM, convolutional neural network, and attention mechanism to extract multiple sentence features and map them to a fixed-dimensional semantic feature vector. By calculating the cosine similarity between the semantic feature vector and the unknown phrase vector, it is judged whether the sentence can explain the semantics of the unknown phrase. And a loss function called positive and negative means square error is introduced to improve the model's ability that distinguishes negative examples. For this task, we provided a Chinese dataset containing phrases and explanation pairs in 7 important domains. On this dataset, TransExplain can achieve better results than previous similar tasks.